An Improved Differential Evolution for Parameter Identification of Photovoltaic Models

Abstract

Photovoltaic (PV) systems are crucial for converting solar energy into electricity. Optimization, control, and simulation for PV systems are important for effectively harnessing solar energy. The exactitude of associated model parameters is an important influencing factor in the performance of PV systems. However, PV model parameter extraction is challenging due to parameter variability resulting from the change in different environmental conditions and equipment factors. Existing parameter identification approaches usually struggle to calculate precise solutions. For this reason, this paper presents an improved differential evolution algorithm, which integrates a collaboration mechanism of dual mutation strategies and an orientation guidance mechanism, called DODE. This collaboration mechanism adaptively assigns mutation strategies to different individuals at different stages to balance exploration and exploitation capabilities. Moreover, an orientation guidance mechanism is proposed to use the information of the movement direction of the population centroid to guide the evolution of elite individuals, preventing them from being trapped in local optima and guiding the population towards a local search. To assess the effectiveness of DODE, comparison experiments were conducted on six different PV models, i.e., the single, double, and triple diode models, and three other commercial PV modules, against ten other excellent meta-heuristic algorithms. For these models, the proposed DODE outperformed other algorithms, with the separate optimal root mean square error values of 9.86021877891317 × 10−4, 9.82484851784979 × 10−4, 9.82484851784993 × 10−4, 2.42507486809489 × 10−3, 1.72981370994064 × 10−3, and 1.66006031250846 × 10−2. Additionally, results obtained from statistical analysis confirm the remarkable competitive superiorities of DODE on convergence rate, stability, and reliability compared with other methods for PV model parameter identification.

Feature Selection by Binary Differential Evolution for Predicting the Energy Production of a Wind Plant

“…Differential evolution (DE) is one of the state-of-the-art methods for optimization [44,45]. The algorithm has been recently modified to improve its capability for finding the optimal solution and for reducing the computational burden in different application domains, such as for the optimization of the operational parameters of an aluminum friction-stir welding process of dissimilar materials (AA6061-T6 and AA5083-H112) [46], for the identification of parameters of photovoltaic models [47], and for the optimal positioning of flexible alternating-current transmission system controllers for reactive power management [48]. In this work, we focus on binary DE (BDE), a variation of DE specifically designed for problems with binary decision spaces.…”

Photovoltaic Modeling: A Comprehensive Analysis of the I–V Characteristic Curve

“…Due to the in-depth understanding of the experimental behavior of PV panels across different ambient conditions, the equivalent-circuit-based models were first developed. These equivalent circuits are a combination of several electrical components including diode(s) and resistors [27][28][29][30][31][32]. By applying circuit analysis, an implicit characteristic equation describing the typical I-V curve has been derived, which varies in mathematical complexity and relies heavily on an accurate estimation of the fitting parameters [33][34][35][36][37][38][39][40].…”

Optimizing Photovoltaic Solar Model Parameters

“…Moreover, algorithms like the Salp Swarm-Inspired Technique [41], the Enhanced Ant Lion Algorithm [38], and the Artificial Bee Swarm [46] have been used. Bio-geography Based Optimization [44], a sophisticated version of the Cuckoo Search Algorithm, Bird Mating Optimization Strategies [45], and the Hybrid Bee Pollinator Flower Pollination Approach have also been applied in this field. This field of study has also benefited from the development of the Artificial Immune System and the Bacterial Foraging Algorithm [43].…”